Impact of Horizontal Resolution on Precipitation Simulation over South Korea with Multi Regional Climate Models

The use of regional climate models (RCMs) can partly reduce the biases in global radiative flux (Eg↓) that are found in reanalysis products and global models, as they allow for a finer spatial resolution and a finer parametrisation of surface and atmospheric processes. In this study, we assess the ability of the MAR («Modèle Atmosphérique Régional») RCM to reproduce observed changes in Eg↓, and we investigate the added value of MAR with respect to reanalyses. Simulations were performed at a horizontal resolution of 5 km for the period 1959–2010 by forcing MAR with different reanalysis products: ERA40/ERA-interim, NCEP/NCAR-v1, ERA-20C, and 20CRV2C. Measurements of Eg↓ from the Global Energy Balance Archive (GEBA) and from the Royal Meteorological Institute of Belgium (RMIB), as well as cloud cover observations from Belgocontrol and RMIB, were used for the evaluation of the MAR model and the forcing reanalyses. Results show that MAR enables largely reducing the mean biases that are present in the reanalyses. The trend analysis shows that only MAR forced by ERA40/ERA-interim shows historical trends, which is probably because the ERA40/ERA-interim has a better horizontal resolution and assimilates more observations than the other reanalyses that are used in this study. The results suggest that the solar brightening observed since the 1980s in Belgium has mainly been due to decreasing cloud cover.

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Deforestation impacts on Amazon-Andes hydroclimatic connectivity

10.5194/egusphere-egu21-9251 ◽

2021 ◽

Author(s):

Juan Sierra ◽

Jhan Carlo Espinoza ◽

Clementine Junquas ◽

Jan Polcher ◽

Miguel Saavedra ◽

...

Keyword(s):

South America ◽

Transition Region ◽

Amazon Basin ◽

Climate Models ◽

Regional Climate ◽

Austral Summer ◽

Regional Climate Models ◽

Horizontal Resolution ◽

Deforestation Rates ◽

Tropical South America

<p>The Amazon rainforest is a key component of the climate system and one of the main planetary evapotranspiration sources. Over the entire Amazon basin, strong land-atmosphere feedbacks cause almost one third of the regional rainfall to be transpired by the local rainforest. Maximum precipitation recycling ratio takes place on the southwestern edge of the Amazon basin (a.k.a. Amazon-Andes transition region), an area recognized as the rainiest and biologically richest of the whole watershed. Here, high precipitation rates lead to large values of runoff per unit area providing most of the sediment load to Amazon rivers. As a consequence, the transition region can potentially be very sensitive to Amazonian forest loss. In fact, recent acceleration in deforestation rates has been reported over tropical South America. These sustained land-cover changes can alter the regional water and energy balances, as well as the regional circulation and rainfall patterns. In this sense, the use of regional climate models can help to understand the possible impacts of deforestation on the Amazon-Andes zone.</p><p>This work aims to assess the projected Amazonian deforestation effects on the moisture transport and rainfall behavior over tropical South America and the Amazon-Andes transition region. We perform 10-year austral summer simulations with the Weather Research and Forecasting model (WRF) using 3 one-way nested domains. Our finest domain is located over the south-western part of the basin, comprising two instrumented Andean Valleys (Zongo and Coroico river Valleys). Convective permitting high horizontal resolution (1km) is used over this domain. The outcomes presented here enhance the understanding of biosphere-atmosphere coupling and its deforestation induced disturbances.</p>

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The impact of RCM formulation and resolution on simulated precipitation in Africa

10.5194/esd-2019-55 ◽

2019 ◽

Cited By ~ 2

Author(s):

Minchao Wu ◽

Grigory Nikulin ◽

Erik Kjellström ◽

Danijel Belušić ◽

Colin Jones ◽

...

Keyword(s):

Climate Models ◽

Regional Climate ◽

Regional Climate Models ◽

Horizontal Resolution ◽

Added Value ◽

Primary Control ◽

Model Formulation ◽

Simulated Precipitation ◽

Mean Climate ◽

The Impact

Abstract. We investigate the impact of model formulation and horizontal resolution on the ability of Regional Climate Models (RCMs) to simulate precipitation in Africa. Two RCMs – SMHI-RCA4 and HCLIM38-ALADIN are utilized for downscaling the ERA-Interim reanalysis over Africa at four different resolutions: 25, 50, 100 and 200 km. Additionally to the two RCMs, two different configurations of the same RCA4 are used. Contrasting different RCMs, configurations and resolutions it is found that model formulation has the primary control over many aspects of the precipitation climatology in Africa. Patterns of spatial biases in seasonal mean precipitation are mostly defined by model formulation while the magnitude of the biases is controlled by resolution. In a similar way, the phase of the diurnal cycle is completely controlled by model formulation (convection scheme) while its amplitude is a function of resolution. Although higher resolution in many cases leads to smaller biases in the time mean climate, the impact of higher resolution is mixed. An improvement in one region/season (e.g. reduction of dry biases) often corresponds to a deterioration in another region/season (e.g. amplification of wet biases). The experiments confirm a pronounced and well known impact of higher resolution – a more realistic distribution of daily precipitation. Even if the time-mean climate is not always greatly sensitive to resolution, what the time-mean climate is made up of, higher order statistics, is sensitive. Therefore, the realism of the simulated precipitation increases as resolution increases. Our results show that improvements in the ability of RCMs to simulate precipitation in Africa compared to their driving reanalysis in many cases are simply related to model formulation and not necessarily to higher resolution. Such model formulation related improvements are strongly model dependent and in general cannot be considered as an added value of downscaling.

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Evaluation of summer precipitation over Far East Asia and South Korea simulated by multiple regional climate models

International Journal of Climatology ◽

10.1002/joc.6331 ◽

2019 ◽

Vol 40 (4) ◽

pp. 2270-2284 ◽

Cited By ~ 3

Author(s):

Changyong Park ◽

Dong‐Hyun Cha ◽

Gayoung Kim ◽

Gil Lee ◽

Dong‐Kyou Lee ◽

...

Keyword(s):

South Korea ◽

East Asia ◽

Climate Models ◽

Regional Climate ◽

Far East ◽

Summer Precipitation ◽

Regional Climate Models

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Evaluation of regional climate models ALARO-0 and REMO2015 at 0.22° resolution over the CORDEX Central Asia domain

Geoscientific Model Development ◽

10.5194/gmd-14-1267-2021 ◽

2021 ◽

Vol 14 (3) ◽

pp. 1267-1293

Author(s):

Sara Top ◽

Lola Kotova ◽

Lesley De Cruz ◽

Svetlana Aniskevich ◽

Leonid Bobylev ◽

...

Keyword(s):

High Resolution ◽

Central Asia ◽

Climate Models ◽

Regional Climate ◽

Regional Climate Models ◽

Horizontal Resolution ◽

Maximum Temperature ◽

The Tibetan Plateau ◽

Climate Information ◽

Climate Data

Abstract. To allow for climate impact studies on human and natural systems, high-resolution climate information is needed. Over some parts of the world plenty of regional climate simulations have been carried out, while in other regions hardly any high-resolution climate information is available. The CORDEX Central Asia domain is one of these regions, and this article describes the evaluation for two regional climate models (RCMs), REMO and ALARO-0, that were run for the first time at a horizontal resolution of 0.22∘ (25 km) over this region. The output of the ERA-Interim-driven RCMs is compared with different observational datasets over the 1980–2017 period. REMO scores better for temperature, whereas the ALARO-0 model prevails for precipitation. Studying specific subregions provides deeper insight into the strengths and weaknesses of both RCMs over the CAS-CORDEX domain. For example, ALARO-0 has difficulties in simulating the temperature over the northern part of the domain, particularly when snow cover is present, while REMO poorly simulates the annual cycle of precipitation over the Tibetan Plateau. The evaluation of minimum and maximum temperature demonstrates that both models underestimate the daily temperature range. This study aims to evaluate whether REMO and ALARO-0 provide reliable climate information over the CAS-CORDEX domain for impact modeling and environmental assessment applications. Depending on the evaluated season and variable, it is demonstrated that the produced climate data can be used in several subregions, e.g., temperature and precipitation over western Central Asia in autumn. At the same time, a bias adjustment is required for regions where significant biases have been identified.

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Evaluation of regional climate models ALARO-0 and REMO2015 at 0.22° resolution over the CORDEX Central Asia domain

10.5194/gmd-2019-368 ◽

2020 ◽

Cited By ~ 1

Author(s):

Sara Top ◽

Lola Kotova ◽

Lesley De Cruz ◽

Svetlana Aniskevich ◽

Leonid Bobylev ◽

...

Keyword(s):

High Resolution ◽

Central Asia ◽

Climate Models ◽

Regional Climate ◽

Regional Climate Models ◽

Horizontal Resolution ◽

Maximum Temperature ◽

Climate Projections ◽

Climate Information ◽

Climate Data

Abstract. To allow for climate impact studies on human and natural systems high-resolution climate information is needed. Over some parts of the world plenty of regional climate simulations have been carried out, while in other regions hardly any high-resolution climate information is available. This publication aims at addressing one of these regional gaps by presenting an evaluation study for two regional climate models (RCMs) (REMO and ALARO-0) at a horizontal resolution of 0.22° (25 km) over Central Asia. The output of the ERA-Interim driven RCMs is compared with different observational datasets over the 1980–2017 period. The choice of the observational dataset has an impact on the scores but in general one can conclude that both models reproduce reasonably well the spatial patterns for temperature and precipitation. The evaluation of minimum and maximum temperature demonstrates that both models underestimate the daily temperature range. More detailed studies of the annual cycle over subregions should be carried out to reveal whether this is due to an incorrect simulation in cloud cover, atmospheric circulation or heat and moisture fluxes. In general, the REMO model scores better for temperature whereas the ALARO-0 model prevails for precipitation. This publication demonstrates that the REMO and ALARO-0 RCMs can be used to perform climate projections over Central Asia and that the produced climate data can be applied in impact modelling.

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Evaluating Snow in EURO-CORDEX Regional Climate Models with Observations for the European Alps: Biases and Their Relationship to Orography, Temperature, and Precipitation Mismatches

Atmosphere ◽

10.3390/atmos11010046 ◽

2019 ◽

Vol 11 (1) ◽

pp. 46

Author(s):

Michael Matiu ◽

Marcello Petitta ◽

Claudia Notarnicola ◽

Marc Zebisch

Keyword(s):

Snow Cover ◽

Snow Depth ◽

Large Scale ◽

Climate Models ◽

Regional Climate ◽

Regional Climate Models ◽

Horizontal Resolution ◽

Winter Period ◽

European Alps ◽

Temperature And Precipitation

Climate models are important tools to assess current and future climate. While they have been extensively used for studying temperature and precipitation, only recently regional climate models (RCMs) arrived at horizontal resolutions that allow studies of snow in complex mountain terrain. Here, we present an evaluation of the snow variables in the World Climate Research Program Coordinated Regional Downscaling Experiment (EURO-CORDEX) RCMs with gridded observations of snow cover (from MODIS remote sensing) and temperature and precipitation (E-OBS), as well as with point (station) observations of snow depth and temperature for the European Alps. Large scale snow cover dynamics were reproduced well with some over- and under-estimations depending on month and RCM. The orography, temperature, and precipitation mismatches could on average explain 31% of the variability in snow cover bias across grid-cells, and even more than 50% in the winter period November–April. Biases in average monthly snow depth were remarkably low for reanalysis driven RCMs (<approx. 30 cm), and large for the GCM driven ones (up to 200 cm), when averaged over all stations within 400 m of altitude difference with RCM orography. Some RCMs indicated low snow cover biases and at the same time high snow depth biases, and vice versa. In summary, RCMs showed good skills in reproducing alpine snow cover conditions with regard to their limited horizontal resolution. Detected shortcomings in the models depended on the considered snow variable, season and individual RCM.

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Added value of the EURO-CORDEX high-resolution downscaling over the Iberian Peninsula revisited. Part II: Max and Min Temperature

10.5194/gmd-2021-208 ◽

2021 ◽

Author(s):

João António Martins Careto ◽

Pedro Miguel Matos Soares ◽

Rita Margarida Cardoso ◽

Sixto Herrera ◽

José Manuel Gutiérrez

Keyword(s):

Iberian Peninsula ◽

Minimum Temperature ◽

Climate Models ◽

Regional Climate ◽

Regional Climate Models ◽

Horizontal Resolution ◽

Added Value ◽

Maximum Temperature ◽

European Continent ◽

Partial Probability

Abstract. In the recent past, the increase of computation resources led to the appearance of regional climate models with increasing domains and resolutions, spamming larger temporal periods. A good example is the World Climate Research Program – Coordinated Regional Climate Downscaling Experiment for the European domain (EURO-CORDEX). This set of regional models encompass the entire European continent, for a 130-year common period until the end of the 21st century, while having a 12 Km horizontal resolution. Such simulations are computationally demanding, while at the same time, not always showing added value. This study considers a recently proposed metric in order to assess the added value of the EURO-CORDEX Hindcast (1989–2008) and Historical (1971–2005) simulations, for the maximum and minimum temperature over the Iberian Peninsula. This approach allows an evaluation of the higher against the driving lower resolutions relative to the performance of the whole or partial probability density functions, by having an observational regular gridded dataset as reference. Overall, the gains for maximum temperature are more relevant in comparison to minimum temperature, partially owed to known problems derived from the snow-albedo-atmosphere feedback. For more local scales, areas near the coast reveal significant added value in comparison with the interior, which displays limited gains and sometimes significant detrimental effects around −30 %. Nevertheless, the added value for temperature extremes reveals a similar range, although with stronger gains in coastal regions and in locations from the interior for maximum temperature, contrasting with the losses for locations in the interior of the domain for the minimum temperature.

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